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Title	Exploring Effective Outlier Detection In Iot: A Systematic Survey Of Techniques And Applications
ID_Doc	25527
Authors	Mosallam B.E.; Ahmed S.H.
Year	2023
Published	1st International Conference of Intelligent Methods, Systems and Applications, IMSA 2023, 2023-January
DOI	http://dx.doi.org/10.1109/IMSA58542.2023.10255071
Abstract	This paper presents a survey of outlier detection techniques used in the analysis of IoT data. We conducted a comprehensive review of existing research studies and identified the most popular techniques for detecting outliers in IoT data, including statistical techniques, machine learning algorithms, and hybrid methods. We also discuss the challenges of IoT, such as data quality, scalability, and security issues, and highlight the importance of outlier detection in various IoT applications, such as healthcare, smart cities, and industrial automation. The results of our survey can be used as a useful resource for researchers and practitioners who are interested in exploring the state-of-the-art techniques in outlier detection for IoT data analysis as it's a very simple guide for any beginner. © 2023 IEEE.
Author Keywords	anomaly; Data quality; IoT; machine learning; noise; outlier detection

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